Evaluation

Evaluation asks whether the model is good enough for the decision, not whether it looks impressive in isolation.

That requires technical metrics and business judgment. False positives and false negatives usually have different costs, and the “best” threshold depends on what action follows the prediction.

The useful output is a decision recommendation: use it, change it, collect more data, or return to the business question.

• Reporting accuracy when the business cost of errors is asymmetric.
• Evaluating on examples that are easier than the real use case.
• Letting a good chart hide a weak operational decision.